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## install.packages('groundhog')[0m
pkgs <- c("tidyverse","here", "lmerTest", "sjPlot","broom.mixed", "kableExtra", "ggeffects", "gt", "brms", "bayestestR","ggdist", "pheatmap", "heatmaply","pheatmap","gplots","RColorBrewer", "tm", "wordcloud", "psych")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)
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## Welcome to heatmaply version 1.3.0
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## Type ?heatmaply for the main documentation.
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## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
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here::i_am("Analysis/idmPrelimAnal.Rmd")
## here() starts at /Users/jacobelder/Documents/GitHub/EpMemNet
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
## ℹ SHA-1 hash of file is "07e3c11d2838efe15b1a6baf5ba2694da3f28cb1"
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
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fullLong <- arrow::read_parquet(here("Data", "longEpMNet.parquet"))
fullShort <- arrow::read_parquet(here("Data","shortEpMNet.parquet"))
fullLong$subID <- as.numeric(fullLong$subID)
fullData <- fullLong %>% full_join(fullShort, by = c("subID"))
# How many people listed 0 connections?
nrow(fullShort[which(fullShort$edgeTot==0),])
## [1] 19
#describe(fullLong$strength)
#Create a vector containing only the text
text <- as.vector(fullData$memory)
# Create a corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
## Warning in tm_map.SimpleCorpus(., removeNumbers): transformation drops documents
## Warning in tm_map.SimpleCorpus(., removePunctuation): transformation drops
## documents
## Warning in tm_map.SimpleCorpus(., stripWhitespace): transformation drops
## documents
docs <- tm_map(docs, content_transformer(tolower))
## Warning in tm_map.SimpleCorpus(docs, content_transformer(tolower)):
## transformation drops documents
docs <- tm_map(docs, removeWords, stopwords("english"))
## Warning in tm_map.SimpleCorpus(docs, removeWords, stopwords("english")):
## transformation drops documents
docs <- tm_map(docs, removeWords, c("the","and"))
## Warning in tm_map.SimpleCorpus(docs, removeWords, c("the", "and")):
## transformation drops documents
dtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df <- data.frame(word = names(words),freq=words)
set.seed(24)
wordcloud(words = df$word, freq = df$freq, min.freq = 1, max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2"))
Yes, the farther away in time, the more experiences something causes.
m<-glmer(outdegree ~ scale(length) + numID + ( scale(length) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(length) + numID + (scale(length) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7829.3 7863.5 -3908.7 7817.3 2209
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3004 -0.9114 -0.1534 0.5892 11.2174
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.23383 0.4836
## scale(length) 0.08941 0.2990 -0.13
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.283669 0.077167 -3.676 0.000237 ***
## scale(length) 0.114670 0.035697 3.212 0.001317 **
## numID 0.038627 0.004514 8.557 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(l)
## scal(lngth) -0.088
## numID -0.816 0.054
Yes, the farther back in time, the fewer experiences cause something.
m<-glmer(indegree ~ scale(length) + numID + ( scale(length) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(length) + numID + (scale(length) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7458.3 7492.5 -3723.1 7446.3 2209
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9442 -0.6458 -0.1297 0.2974 9.5454
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.26312 0.5130
## scale(length) 0.07689 0.2773 -0.25
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.185839 0.078373 -2.371 0.0177 *
## scale(length) -0.147931 0.037409 -3.954 7.67e-05 ***
## numID 0.033445 0.004683 7.142 9.20e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(l)
## scal(lngth) 0.004
## numID -0.809 -0.027
More positive and negative, experience causes more experience
More positive, experience is caused by more experiences
m<-glmer(outdegree ~ scale(positive) + scale(negative) + numID + ( scale(positive) + scale(negative) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## outdegree ~ scale(positive) + scale(negative) + numID + (scale(positive) +
## scale(negative) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 5185.0 5237.5 -2582.5 5165.0 1394
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1054 -0.9130 -0.1103 0.6085 7.7908
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.16785 0.4097
## scale(positive) 0.05166 0.2273 -0.07
## scale(negative) 0.04591 0.2143 -0.20 0.83
## Number of obs: 1404, groups: subID, 204
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.096675 0.076445 -1.265 0.20600
## scale(positive) 0.136995 0.044201 3.099 0.00194 **
## scale(negative) 0.152951 0.046860 3.264 0.00110 **
## numID 0.039450 0.004358 9.053 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(p) scl(n)
## scale(pstv) 0.151
## scale(ngtv) 0.137 0.771
## numID -0.816 -0.026 -0.040
m<-glmer(indegree ~ scale(positive) + scale(negative) + numID + ( scale(positive) + scale(negative) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0114173 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## indegree ~ scale(positive) + scale(negative) + numID + (scale(positive) +
## scale(negative) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 5028.9 5081.4 -2504.5 5008.9 1394
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3588 -0.6642 -0.1111 0.3082 8.6045
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.238347 0.48821
## scale(positive) 0.006408 0.08005 0.22
## scale(negative) 0.067935 0.26064 0.23 1.00
## Number of obs: 1404, groups: subID, 204
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.121492 0.081602 -1.489 0.136533
## scale(positive) 0.138182 0.039441 3.504 0.000459 ***
## scale(negative) 0.056178 0.051405 1.093 0.274466
## numID 0.035139 0.004771 7.364 1.78e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(p) scl(n)
## scale(pstv) 0.145
## scale(ngtv) 0.182 0.732
## numID -0.788 0.056 0.038
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0114173 (tol = 0.002, component 1)
m<-lmer(positive ~ outdegree + indegree + numID + ( outdegree + indegree | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive ~ outdegree + indegree + numID + (outdegree + indegree |
## subID)
## Data: fullData
##
## REML criterion at convergence: 18148.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0855 -0.4876 0.2808 0.6541 2.0780
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 254.519 15.954
## outdegree 3.762 1.940 -0.44
## indegree 2.425 1.557 -0.61 0.04
## Residual 809.399 28.450
## Number of obs: 1879, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 71.6835 2.1169 304.3606 33.863 <2e-16 ***
## outdegree -0.1219 0.4417 62.4226 -0.276 0.7834
## indegree 1.1853 0.4287 27.0244 2.765 0.0101 *
## numID -0.1147 0.1329 177.1610 -0.863 0.3891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) outdgr indegr
## outdegree -0.166
## indegree -0.145 -0.104
## numID -0.654 -0.208 -0.265
m<-lmer(negative ~ outdegree + indegree + numID + ( outdegree + indegree | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: negative ~ outdegree + indegree + numID + (outdegree + indegree |
## subID)
## Data: fullData
##
## REML criterion at convergence: 15342.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8807 -0.7812 -0.1741 0.7629 2.5569
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 308.115 17.553
## outdegree 1.812 1.346 -0.20
## indegree 6.301 2.510 -0.63 0.41
## Residual 1032.610 32.134
## Number of obs: 1548, groups: subID, 207
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.5571 2.5658 265.9755 15.807 <2e-16 ***
## outdegree 0.2995 0.4915 36.2620 0.609 0.5461
## indegree -1.0552 0.5875 37.7387 -1.796 0.0805 .
## numID 0.1652 0.1586 163.0605 1.042 0.2992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) outdgr indegr
## outdegree -0.182
## indegree -0.202 0.008
## numID -0.664 -0.163 -0.238
More positive and more negative, experience causes more experiences
m<-glmer(outdegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + ( scale(PANAS_P) + scale(PANAS_N) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0420052 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## outdegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + (scale(PANAS_P) +
## scale(PANAS_N) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7289.2 7345.3 -3634.6 7269.2 2014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2547 -0.9050 -0.1423 0.5655 8.0179
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21270 0.4612
## scale(PANAS_P) 0.08237 0.2870 -0.14
## scale(PANAS_N) 0.05482 0.2341 -0.37 0.82
## Number of obs: 2024, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.270975 0.073638 -3.680 0.000233 ***
## scale(PANAS_P) 0.130171 0.040121 3.244 0.001177 **
## scale(PANAS_N) 0.203885 0.036301 5.617 1.95e-08 ***
## numID 0.041642 0.004371 9.526 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANAS_P s(PANAS_N
## sc(PANAS_P) -0.096
## sc(PANAS_N) -0.121 0.733
## numID -0.807 0.033 -0.025
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0420052 (tol = 0.002, component 1)
m<-glmer(outdegree ~ scale(PANAS_1) + numID + ( scale(PANAS_1) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_1) + numID + (scale(PANAS_1) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7382.6 7416.3 -3685.3 7370.6 2020
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1696 -0.9468 -0.1457 0.6021 8.5283
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2123 0.4607
## scale(PANAS_1) 0.0365 0.1910 0.09
## Number of obs: 2026, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.219332 0.072973 -3.006 0.00265 **
## scale(PANAS_1) -0.005384 0.028583 -0.188 0.85059
## numID 0.038786 0.004372 8.871 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_1) -0.006
## numID -0.811 0.029
m<-glmer(outdegree ~ scale(PANAS_2) + numID + ( scale(PANAS_2) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_2) + numID + (scale(PANAS_2) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7360.3 7394.0 -3674.2 7348.3 2014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2057 -0.9420 -0.1321 0.5946 8.5392
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21560 0.4643
## scale(PANAS_2) 0.03714 0.1927 0.11
## Number of obs: 2020, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.223538 0.073380 -3.046 0.00232 **
## scale(PANAS_2) -0.012244 0.028681 -0.427 0.66946
## numID 0.038849 0.004397 8.835 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_2) 0.002
## numID -0.811 0.034
m<-glmer(outdegree ~ scale(PANAS_3) + numID + ( scale(PANAS_3) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_3) + numID + (scale(PANAS_3) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7388.5 7422.1 -3688.2 7376.5 2018
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2179 -0.9614 -0.1375 0.6147 8.7183
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2116 0.460
## scale(PANAS_3) 0.0279 0.167 0.04
## Number of obs: 2024, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.215610 0.072760 -2.963 0.00304 **
## scale(PANAS_3) -0.007147 0.026949 -0.265 0.79086
## numID 0.038851 0.004364 8.903 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_3) -0.011
## numID -0.812 0.022
m<-glmer(outdegree ~ scale(PANAS_4) + numID + ( scale(PANAS_4) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00277919 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_4) + numID + (scale(PANAS_4) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7355.5 7389.2 -3671.8 7343.5 2010
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1589 -0.9462 -0.1283 0.6123 8.6525
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2165 0.4653
## scale(PANAS_4) 0.0315 0.1775 0.05
## Number of obs: 2016, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.210564 0.073286 -2.873 0.00406 **
## scale(PANAS_4) -0.010645 0.027558 -0.386 0.69929
## numID 0.038464 0.004405 8.733 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_4) 0.000
## numID -0.811 0.019
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00277919 (tol = 0.002, component 1)
m<-glmer(outdegree ~ scale(PANAS_5) + numID + ( scale(PANAS_5) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00225143 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_5) + numID + (scale(PANAS_5) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7326.7 7360.3 -3657.4 7314.7 2007
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2607 -0.9321 -0.1393 0.6102 8.3581
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.20451 0.4522
## scale(PANAS_5) 0.05001 0.2236 0.02
## Number of obs: 2013, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.222421 0.072794 -3.055 0.00225 **
## scale(PANAS_5) 0.012754 0.030668 0.416 0.67749
## numID 0.038413 0.004343 8.844 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_5) -0.035
## numID -0.811 0.037
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00225143 (tol = 0.002, component 1)
m<-glmer(outdegree ~ scale(PANAS_6) + numID + ( scale(PANAS_6) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_6) + numID + (scale(PANAS_6) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7289.7 7323.3 -3638.8 7277.7 1998
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1622 -0.9458 -0.1438 0.5814 10.2155
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22693 0.4764
## scale(PANAS_6) 0.02762 0.1662 -0.27
## Number of obs: 2004, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.208671 0.073557 -2.837 0.00456 **
## scale(PANAS_6) 0.067077 0.026218 2.558 0.01051 *
## numID 0.038362 0.004439 8.642 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_6) -0.056
## numID -0.807 -0.032
m<-glmer(outdegree ~ scale(PANAS_7) + numID + ( scale(PANAS_7) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00227512 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_7) + numID + (scale(PANAS_7) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7332.8 7366.5 -3660.4 7320.8 2009
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1972 -0.9538 -0.1640 0.6094 9.7612
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22509 0.4744
## scale(PANAS_7) 0.02393 0.1547 -0.32
## Number of obs: 2015, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.223109 0.073441 -3.038 0.002382 **
## scale(PANAS_7) 0.085580 0.025708 3.329 0.000872 ***
## numID 0.039254 0.004401 8.919 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_7) -0.074
## numID -0.806 -0.032
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00227512 (tol = 0.002, component 1)
m<-glmer(outdegree ~ scale(PANAS_8) + numID + ( scale(PANAS_8) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_8) + numID + (scale(PANAS_8) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7345.6 7379.2 -3666.8 7333.6 2011
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2594 -0.9539 -0.1602 0.5810 9.9169
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22548 0.4748
## scale(PANAS_8) 0.02235 0.1495 -0.42
## Number of obs: 2017, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.227962 0.072882 -3.128 0.00176 **
## scale(PANAS_8) 0.107117 0.024562 4.361 1.29e-05 ***
## numID 0.040131 0.004345 9.237 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_8) -0.088
## numID -0.804 -0.066
m<-glmer(outdegree ~ scale(PANAS_9) + numID + ( scale(PANAS_9) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_9) + numID + (scale(PANAS_9) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7321.1 7354.7 -3654.5 7309.1 2012
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2537 -0.9417 -0.1545 0.5878 9.8810
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22060 0.4697
## scale(PANAS_9) 0.02037 0.1427 -0.35
## Number of obs: 2018, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.217340 0.072759 -2.987 0.002816 **
## scale(PANAS_9) 0.089319 0.024566 3.636 0.000277 ***
## numID 0.038945 0.004356 8.940 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_9) -0.071
## numID -0.807 -0.048
m<-glmer(outdegree ~ scale(PANAS_10) + numID + ( scale(PANAS_10) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_10) + numID + (scale(PANAS_10) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7333.4 7367.0 -3660.7 7321.4 2009
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0771 -0.9399 -0.1344 0.6036 10.4137
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21964 0.4687
## scale(PANAS_10) 0.02394 0.1547 -0.42
## Number of obs: 2015, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.222271 0.072005 -3.087 0.00202 **
## scale(PANAS_10) 0.113663 0.025030 4.541 5.59e-06 ***
## numID 0.039908 0.004289 9.305 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## s(PANAS_10) -0.078
## numID -0.803 -0.074
More positive, more experiences cause an experience
m<-glmer(indegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + ( scale(PANAS_P) + scale(PANAS_N) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0150649 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## indegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + (scale(PANAS_P) +
## scale(PANAS_N) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 6999.3 7055.5 -3489.7 6979.3 2014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7296 -0.6425 -0.1311 0.2854 8.9860
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.20783 0.4559
## scale(PANAS_P) 0.03141 0.1772 -0.15
## scale(PANAS_N) 0.03976 0.1994 0.02 0.63
## Number of obs: 2024, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.207496 0.073023 -2.842 0.00449 **
## scale(PANAS_P) 0.098100 0.034287 2.861 0.00422 **
## scale(PANAS_N) 0.021779 0.035710 0.610 0.54194
## numID 0.038190 0.004422 8.637 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANAS_P s(PANAS_N
## sc(PANAS_P) -0.062
## sc(PANAS_N) -0.068 0.648
## numID -0.813 0.007 0.090
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0150649 (tol = 0.002, component 1)
m<-glmer(indegree ~ scale(PANAS_1) + numID + ( scale(PANAS_1) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_1) + numID + (scale(PANAS_1) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7057.8 7091.5 -3522.9 7045.8 2020
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3746 -0.6618 -0.1311 0.2898 10.6200
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21300 0.4615
## scale(PANAS_1) 0.02068 0.1438 -0.17
## Number of obs: 2026, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193577 0.072274 -2.678 0.0074 **
## scale(PANAS_1) 0.065117 0.025886 2.516 0.0119 *
## numID 0.037734 0.004356 8.662 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_1) -0.018
## numID -0.811 -0.059
m<-glmer(indegree ~ scale(PANAS_2) + numID + ( scale(PANAS_2) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_2) + numID + (scale(PANAS_2) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7017.8 7051.5 -3502.9 7005.8 2014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3750 -0.6805 -0.1314 0.2929 9.5407
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.20989 0.4581
## scale(PANAS_2) 0.03127 0.1768 -0.20
## Number of obs: 2020, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193787 0.072165 -2.685 0.00725 **
## scale(PANAS_2) 0.069461 0.027994 2.481 0.01309 *
## numID 0.037141 0.004341 8.556 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_2) -0.018
## numID -0.809 -0.081
m<-glmer(indegree ~ scale(PANAS_3) + numID + ( scale(PANAS_3) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_3) + numID + (scale(PANAS_3) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7020.7 7054.4 -3504.3 7008.7 2018
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3803 -0.6669 -0.1259 0.2769 10.5421
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21454 0.4632
## scale(PANAS_3) 0.02466 0.1570 -0.17
## Number of obs: 2024, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.199603 0.072439 -2.755 0.005861 **
## scale(PANAS_3) 0.090778 0.026861 3.379 0.000726 ***
## numID 0.037541 0.004365 8.601 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_3) -0.032
## numID -0.809 -0.056
m<-glmer(indegree ~ scale(PANAS_4) + numID + ( scale(PANAS_4) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_4) + numID + (scale(PANAS_4) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7002.3 7036.0 -3495.2 6990.3 2010
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3712 -0.7003 -0.1421 0.2903 10.5508
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21145 0.4598
## scale(PANAS_4) 0.02809 0.1676 -0.27
## Number of obs: 2016, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.196896 0.072056 -2.733 0.00628 **
## scale(PANAS_4) 0.083715 0.027408 3.054 0.00225 **
## numID 0.037457 0.004321 8.669 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_4) -0.022
## numID -0.808 -0.097
m<-glmer(indegree ~ scale(PANAS_5) + numID + ( scale(PANAS_5) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00524094 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_5) + numID + (scale(PANAS_5) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 6983.8 7017.5 -3485.9 6971.8 2007
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3954 -0.6967 -0.1409 0.2874 9.6745
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21323 0.4618
## scale(PANAS_5) 0.02621 0.1619 -0.16
## Number of obs: 2013, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.212147 0.072628 -2.921 0.003489 **
## scale(PANAS_5) 0.103939 0.026837 3.873 0.000108 ***
## numID 0.038484 0.004355 8.837 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_5) -0.065
## numID -0.809 -0.013
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00524094 (tol = 0.002, component 1)
m<-glmer(indegree ~ scale(PANAS_6) + numID + ( scale(PANAS_6) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00247089 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_6) + numID + (scale(PANAS_6) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 6962.3 6995.9 -3475.1 6950.3 1998
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4588 -0.6833 -0.1260 0.3078 9.0008
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2118 0.4602
## scale(PANAS_6) 0.0233 0.1526 0.11
## Number of obs: 2004, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.194635 0.072596 -2.681 0.00734 **
## scale(PANAS_6) -0.051159 0.026512 -1.930 0.05365 .
## numID 0.037377 0.004391 8.512 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_6) -0.020
## numID -0.812 0.085
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00247089 (tol = 0.002, component 1)
m<-glmer(indegree ~ scale(PANAS_7) + numID + ( scale(PANAS_7) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_7) + numID + (scale(PANAS_7) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7024.0 7057.7 -3506.0 7012.0 2009
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2003 -0.6976 -0.1192 0.2948 8.8923
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22212 0.4713
## scale(PANAS_7) 0.02101 0.1450 0.06
## Number of obs: 2015, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193717 0.073828 -2.624 0.00869 **
## scale(PANAS_7) -0.017714 0.026404 -0.671 0.50229
## numID 0.037200 0.004477 8.309 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_7) -0.043
## numID -0.812 0.084
m<-glmer(indegree ~ scale(PANAS_8) + numID + ( scale(PANAS_8) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_8) + numID + (scale(PANAS_8) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7025.2 7058.8 -3506.6 7013.2 2011
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2262 -0.6747 -0.1146 0.3114 9.3700
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2174 0.4663
## scale(PANAS_8) 0.0218 0.1477 0.16
## Number of obs: 2017, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.188103 0.073252 -2.568 0.0102 *
## scale(PANAS_8) -0.028800 0.026068 -1.105 0.2693
## numID 0.036948 0.004441 8.320 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_8) -0.048
## numID -0.813 0.118
m<-glmer(indegree ~ scale(PANAS_9) + numID + ( scale(PANAS_9) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_9) + numID + (scale(PANAS_9) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7035.7 7069.4 -3511.9 7023.7 2012
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2298 -0.6648 -0.1104 0.2917 9.3696
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2173 0.4662
## scale(PANAS_9) 0.0234 0.1530 0.12
## Number of obs: 2018, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.19218 0.07328 -2.623 0.00873 **
## scale(PANAS_9) -0.02539 0.02613 -0.972 0.33127
## numID 0.03747 0.00444 8.439 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_9) -0.035
## numID -0.813 0.092
m<-glmer(indegree ~ scale(PANAS_10) + numID + ( scale(PANAS_10) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_10) + numID + (scale(PANAS_10) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7009.5 7043.2 -3498.8 6997.5 2009
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4484 -0.6821 -0.1305 0.2984 10.0272
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21470 0.4634
## scale(PANAS_10) 0.01904 0.1380 0.09
## Number of obs: 2015, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.190824 0.073199 -2.607 0.00914 **
## scale(PANAS_10) -0.024909 0.025675 -0.970 0.33197
## numID 0.037077 0.004458 8.317 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## s(PANAS_10) -0.052
## numID -0.814 0.110
Causing more experiences and being caused by more experiences is associated with greater certainty in experience, but causing is a stronger effect.
Using strength/similarity is stronger effect.
m<-lmer(scale(Cert) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Cert) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5031.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6090 -0.5059 0.1124 0.5878 2.8176
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.307234 0.55429
## scale(outdegree) 0.057874 0.24057 -0.25
## scale(indegree) 0.006803 0.08248 -0.30 -0.19
## Residual 0.597374 0.77290
## Number of obs: 1978, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.477e-01 7.699e-02 2.521e+02 1.918 0.05623 .
## scale(outdegree) 9.801e-02 3.474e-02 8.611e+01 2.821 0.00594 **
## scale(indegree) 5.612e-02 2.643e-02 1.985e+01 2.124 0.04648 *
## numID -7.075e-03 5.004e-03 1.778e+02 -1.414 0.15918
## scale(length) -1.175e-01 2.153e-02 1.949e+03 -5.457 5.45e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.193
## scale(ndgr) 0.139 -0.114
## numID -0.807 -0.229 -0.180
## scal(lngth) -0.081 -0.074 0.118 0.049
m<-lmer(scale(Cert) ~ scale(strengthIn) + scale(strengthOut) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Cert) ~ scale(strengthIn) + scale(strengthOut) + (scale(strengthIn) +
## scale(strengthOut) | subID)
## Data: fullData
##
## REML criterion at convergence: 5182.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7369 -0.5148 0.1246 0.5897 2.6249
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.29490 0.5430
## scale(strengthIn) 0.01240 0.1113 -0.23
## scale(strengthOut) 0.02925 0.1710 -0.43 -0.26
## Residual 0.60773 0.7796
## Number of obs: 2044, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05506 0.04389 206.39256 1.255 0.211075
## scale(strengthIn) 0.09384 0.02863 24.99336 3.278 0.003068 **
## scale(strengthOut) 0.11503 0.02895 60.50874 3.974 0.000191 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scl(strngI) 0.025
## scl(strngO) -0.085 -0.275
Experiences causing more experiences are more predictive of clarity than experiences caused by more experiences
m<-lmer( scale(Clear) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Clear) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5140.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2286 -0.4757 0.1554 0.6171 3.2107
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.289103 0.53768
## scale(outdegree) 0.034093 0.18464 -0.16
## scale(indegree) 0.005779 0.07602 -0.16 -0.57
## Residual 0.630087 0.79378
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.890e-01 7.424e-02 2.564e+02 2.546 0.0115 *
## scale(outdegree) 7.679e-02 3.105e-02 8.094e+01 2.473 0.0155 *
## scale(indegree) 3.706e-02 2.571e-02 1.740e+01 1.441 0.1673
## numID -9.836e-03 4.836e-03 1.801e+02 -2.034 0.0434 *
## scale(length) -1.188e-01 2.180e-02 1.974e+03 -5.449 5.69e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.176
## scale(ndgr) 0.118 -0.221
## numID -0.801 -0.182 -0.142
## scal(lngth) -0.081 -0.077 0.129 0.049
m<-lmer( scale(Clear) ~ scale(strengthIn) + scale(strengthOut) + numID + scale(length) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00223588 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Clear) ~ scale(strengthIn) + scale(strengthOut) + numID +
## scale(length) + (scale(strengthIn) + scale(strengthOut) | subID)
## Data: fullData
##
## REML criterion at convergence: 5131.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3406 -0.4673 0.1358 0.6127 3.2332
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.286449 0.53521
## scale(strengthIn) 0.008327 0.09125 -0.22
## scale(strengthOut) 0.018026 0.13426 -0.38 -0.30
## Residual 0.632690 0.79542
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.191e-01 7.461e-02 2.635e+02 2.936 0.00362 **
## scale(strengthIn) 5.984e-02 2.784e-02 2.935e+01 2.150 0.03996 *
## scale(strengthOut) 1.200e-01 2.802e-02 6.655e+01 4.282 6.08e-05 ***
## numID -1.143e-02 4.831e-03 1.891e+02 -2.367 0.01897 *
## scale(length) -1.176e-01 2.167e-02 1.958e+03 -5.428 6.40e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I) scl(O) numID
## scl(strngI) 0.140
## scl(strngO) 0.172 -0.229
## numID -0.807 -0.163 -0.244
## scal(lngth) -0.074 0.135 -0.065 0.043
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00223588 (tol = 0.002, component 1)
The number of an experiences of causes, but not what it is caused by, predict how fundamental an experience is.
m<-lmer( scale(Fund) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Fund) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5206.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2467 -0.5500 0.1269 0.6305 2.7223
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.23053 0.48013
## scale(outdegree) 0.01731 0.13157 -0.62
## scale(indegree) 0.00201 0.04484 0.11 0.34
## Residual 0.67263 0.82014
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.030e-01 7.013e-02 2.787e+02 4.321 2.17e-05 ***
## scale(outdegree) 2.411e-01 2.681e-02 6.215e+01 8.990 7.56e-13 ***
## scale(indegree) 5.018e-02 2.397e-02 6.440e+00 2.094 0.077981 .
## numID -1.653e-02 4.445e-03 1.902e+02 -3.719 0.000263 ***
## scale(length) 4.230e-02 2.193e-02 1.854e+03 1.929 0.053890 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.213
## scale(ndgr) 0.112 -0.019
## numID -0.811 -0.345 -0.077
## scal(lngth) -0.100 -0.089 0.130 0.070
m<-lmer( scale(Fund) ~ scale(strengthIn) + scale(strengthOut) + numID + scale(length) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Fund) ~ scale(strengthIn) + scale(strengthOut) + numID +
## scale(length) + (scale(strengthIn) + scale(strengthOut) | subID)
## Data: fullData
##
## REML criterion at convergence: 5172.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3264 -0.5650 0.1118 0.6422 2.8645
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.231291 0.48093
## scale(strengthIn) 0.006982 0.08356 0.12
## scale(strengthOut) 0.013599 0.11661 -0.67 0.66
## Residual 0.659268 0.81195
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.393e-01 7.025e-02 2.909e+02 4.830 2.21e-06 ***
## scale(strengthIn) 7.023e-02 2.714e-02 4.316e+01 2.588 0.0131 *
## scale(strengthOut) 2.688e-01 2.596e-02 9.901e+01 10.357 < 2e-16 ***
## numID -1.866e-02 4.431e-03 1.980e+02 -4.211 3.85e-05 ***
## scale(length) 5.370e-02 2.172e-02 1.945e+03 2.473 0.0135 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I) scl(O) numID
## scl(strngI) 0.130
## scl(strngO) 0.247 0.070
## numID -0.808 -0.049 -0.379
## scal(lngth) -0.087 0.131 -0.066 0.059
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer(scale(PCAimp) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(PCAimp) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5053.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3852 -0.5216 0.0902 0.6224 2.7075
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.268839 0.51850
## scale(outdegree) 0.016052 0.12670 -0.60
## scale(indegree) 0.007867 0.08869 0.03 0.07
## Residual 0.608755 0.78023
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.677e-01 7.242e-02 2.756e+02 5.078 7.03e-07 ***
## scale(outdegree) 2.304e-01 2.594e-02 7.478e+01 8.879 2.58e-13 ***
## scale(indegree) 6.813e-02 2.726e-02 2.695e+01 2.499 0.0188 *
## numID -1.982e-02 4.664e-03 1.978e+02 -4.249 3.31e-05 ***
## scale(length) 3.321e-02 2.115e-02 1.903e+03 1.570 0.1165
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.199
## scale(ndgr) 0.130 -0.051
## numID -0.805 -0.332 -0.091
## scal(lngth) -0.091 -0.086 0.118 0.063
m<-lmer(scale(PCAimp) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(PCAimp) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5011.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4462 -0.5297 0.0793 0.6167 2.7160
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.27011 0.5197
## scale(strengthOut) 0.01080 0.1039 -0.73
## scale(strengthIn) 0.01076 0.1037 0.24 0.49
## Residual 0.59734 0.7729
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.079e-01 7.178e-02 2.872e+02 5.682 3.27e-08 ***
## scale(strengthOut) 2.607e-01 2.420e-02 1.282e+02 10.773 < 2e-16 ***
## scale(strengthIn) 9.057e-02 2.829e-02 4.022e+01 3.201 0.00267 **
## numID -2.222e-02 4.596e-03 2.049e+02 -4.833 2.63e-06 ***
## scale(length) 4.612e-02 2.086e-02 1.960e+03 2.211 0.02717 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.247
## scl(strngI) 0.121 0.065
## numID -0.798 -0.399 0.000
## scal(lngth) -0.081 -0.064 0.130 0.056
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Experiences that cause more experiences are perceived as important to self, but not experiences that are caused by more experiences.
m<-lmer( scale(IM) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(IM) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5312.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4772 -0.4132 0.2034 0.5973 2.5891
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.1893444 0.43514
## scale(outdegree) 0.0246494 0.15700 -0.37
## scale(indegree) 0.0004888 0.02211 -0.98 0.15
## Residual 0.7178590 0.84727
## Number of obs: 2002, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.489e-01 6.778e-02 3.037e+02 5.148 4.75e-07 ***
## scale(outdegree) 1.583e-01 2.951e-02 9.646e+01 5.363 5.60e-07 ***
## scale(indegree) 7.053e-02 2.183e-02 1.325e+03 3.231 0.00126 **
## numID -1.917e-02 4.238e-03 1.972e+02 -4.523 1.05e-05 ***
## scale(length) -6.436e-02 2.254e-02 1.922e+03 -2.855 0.00434 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.205
## scale(ndgr) 0.143 -0.093
## numID -0.820 -0.264 -0.205
## scal(lngth) -0.102 -0.085 0.132 0.068
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(IM) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(IM) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5272.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4657 -0.4128 0.1911 0.5905 2.5754
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.182713 0.42745
## scale(strengthOut) 0.023005 0.15167 -0.55
## scale(strengthIn) 0.001829 0.04277 -0.16 0.91
## Residual 0.703560 0.83879
## Number of obs: 2002, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.889e-01 6.728e-02 3.229e+02 5.780 1.76e-08 ***
## scale(strengthOut) 1.966e-01 2.923e-02 9.618e+01 6.725 1.25e-09 ***
## scale(strengthIn) 7.710e-02 2.432e-02 7.275e+01 3.171 0.00223 **
## numID -2.149e-02 4.156e-03 2.061e+02 -5.171 5.50e-07 ***
## scale(length) -5.727e-02 2.223e-02 1.919e+03 -2.576 0.01006 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.238
## scl(strngI) 0.138 0.014
## numID -0.821 -0.341 -0.120
## scal(lngth) -0.096 -0.069 0.135 0.064
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Experiences that cause more experiences are perceived as important to others, but not experiences that are caused by more experiences.
m<-lmer( scale(IO) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(IO) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5386.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2061 -0.6042 0.1366 0.6781 2.3626
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.18664 0.4320
## scale(outdegree) 0.01514 0.1231 -0.48
## scale(indegree) 0.01821 0.1349 -0.39 -0.04
## Residual 0.74699 0.8643
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.987e-01 6.882e-02 2.856e+02 2.887 0.00418 **
## scale(outdegree) 1.265e-01 2.767e-02 5.062e+01 4.572 3.14e-05 ***
## scale(indegree) 4.480e-02 3.230e-02 2.837e+01 1.387 0.17627
## numID -1.081e-02 4.271e-03 1.812e+02 -2.530 0.01226 *
## scale(length) -4.575e-02 2.294e-02 1.873e+03 -1.995 0.04622 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.185
## scale(ndgr) 0.178 -0.135
## numID -0.826 -0.274 -0.235
## scal(lngth) -0.099 -0.091 0.104 0.066
m<-lmer( scale(IO) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(IO) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5374.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2080 -0.6087 0.1441 0.6728 2.5154
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.18470 0.4298
## scale(strengthOut) 0.01045 0.1022 -0.56
## scale(strengthIn) 0.01748 0.1322 -0.32 0.32
## Residual 0.74478 0.8630
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.324e-01 6.886e-02 2.932e+02 3.375 0.000838 ***
## scale(strengthOut) 1.422e-01 2.674e-02 4.664e+01 5.320 2.88e-06 ***
## scale(strengthIn) 7.125e-02 3.283e-02 3.189e+01 2.170 0.037542 *
## numID -1.272e-02 4.247e-03 1.863e+02 -2.995 0.003122 **
## scale(length) -3.765e-02 2.283e-02 1.893e+03 -1.649 0.099224 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.205
## scl(strngI) 0.191 -0.074
## numID -0.825 -0.292 -0.211
## scal(lngth) -0.088 -0.068 0.111 0.056
No evidence
fullData$ImpDiff <- (fullData$IM-fullData$IO)
m<-lmer( scale(ImpDiff) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(ImpDiff) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5523
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3018 -0.5933 -0.2058 0.5344 3.5220
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.15512 0.3939
## scale(strengthOut) 0.01130 0.1063 -0.19
## scale(strengthIn) 0.01862 0.1364 -0.13 -0.29
## Residual 0.81724 0.9040
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.053e-02 6.595e-02 2.824e+02 0.766 0.444
## scale(strengthOut) -9.043e-03 2.850e-02 6.654e+01 -0.317 0.752
## scale(strengthIn) -7.300e-03 3.445e-02 2.686e+01 -0.212 0.834
## numID -3.020e-03 4.014e-03 1.675e+02 -0.752 0.453
## scale(length) -2.838e-03 2.357e-02 1.831e+03 -0.120 0.904
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.170
## scl(strngI) 0.177 -0.231
## numID -0.821 -0.185 -0.165
## scal(lngth) -0.092 -0.067 0.120 0.057
m<-lmer( scale(ImpDiff) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(ImpDiff) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5522
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2377 -0.5761 -0.2066 0.5357 3.5269
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.15463 0.3932
## scale(outdegree) 0.01526 0.1235 -0.19
## scale(indegree) 0.02248 0.1499 -0.21 -0.40
## Residual 0.81309 0.9017
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.008e-02 6.599e-02 2.830e+02 0.759 0.449
## scale(outdegree) -1.517e-02 2.885e-02 6.510e+01 -0.526 0.601
## scale(indegree) 2.418e-03 3.472e-02 2.524e+01 0.070 0.945
## numID -2.986e-03 4.031e-03 1.665e+02 -0.741 0.460
## scale(length) 7.946e-04 2.361e-02 1.825e+03 0.034 0.973
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.169
## scale(ndgr) 0.171 -0.227
## numID -0.824 -0.190 -0.189
## scal(lngth) -0.103 -0.088 0.107 0.067
Experiences with more causes and caused by more are reflected on more frequently. Perhaps some stronger effects for causing more.
m<-lmer( scale(Often) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Often) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5125.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5447 -0.6743 -0.0551 0.6695 2.7754
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.280069 0.52922
## scale(outdegree) 0.041688 0.20418 0.03
## scale(indegree) 0.002115 0.04599 -1.00 -0.06
## Residual 0.645235 0.80326
## Number of obs: 1977, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.43322 0.07428 264.34044 5.832 1.59e-08 ***
## scale(outdegree) 0.12297 0.03281 64.99111 3.748 0.000381 ***
## scale(indegree) 0.07828 0.02169 276.24537 3.608 0.000366 ***
## numID -0.02422 0.00480 185.07745 -5.045 1.08e-06 ***
## scale(length) -0.10483 0.02210 1959.37290 -4.744 2.25e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.195
## scale(ndgr) 0.177 -0.074
## numID -0.802 -0.126 -0.307
## scal(lngth) -0.075 -0.074 0.132 0.038
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(Often) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -6.5e+02
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Often) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5117.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5156 -0.6768 -0.0391 0.6767 2.8425
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 2.769e-01 0.5262348
## scale(strengthOut) 1.763e-07 0.0004199 -1.00
## scale(strengthIn) 1.115e-02 0.1055889 -0.38 0.38
## Residual 6.574e-01 0.8108015
## Number of obs: 1977, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.661e-01 7.334e-02 2.608e+02 6.356 9.22e-10 ***
## scale(strengthOut) 1.587e-01 2.158e-02 1.295e+03 7.353 3.41e-13 ***
## scale(strengthIn) 1.162e-01 2.919e-02 2.284e+01 3.980 0.000597 ***
## numID -2.585e-02 4.755e-03 1.869e+02 -5.437 1.68e-07 ***
## scale(length) -9.569e-02 2.175e-02 1.957e+03 -4.400 1.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.129
## scl(strngI) 0.182 -0.141
## numID -0.802 -0.118 -0.230
## scal(lngth) -0.061 -0.043 0.132 0.029
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(Chan) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Chan) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5216.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5976 -0.5322 0.1484 0.6224 2.7493
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2310589 0.48069
## scale(outdegree) 0.0135199 0.11628 -0.62
## scale(indegree) 0.0001786 0.01336 -0.18 0.88
## Residual 0.6790026 0.82402
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.569e-01 7.019e-02 2.751e+02 5.085 6.81e-07 ***
## scale(outdegree) 2.149e-01 2.586e-02 8.309e+01 8.309 1.57e-12 ***
## scale(indegree) 4.684e-02 2.174e-02 1.191e+02 2.154 0.0332 *
## numID -1.975e-02 4.464e-03 1.889e+02 -4.424 1.64e-05 ***
## scale(length) 3.090e-02 2.198e-02 1.889e+03 1.405 0.1601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.208
## scale(ndgr) 0.114 -0.039
## numID -0.812 -0.332 -0.113
## scal(lngth) -0.098 -0.090 0.140 0.068
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(Chan) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Chan) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5179.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7230 -0.5164 0.1368 0.6088 2.8039
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.234197 0.48394
## scale(strengthOut) 0.011446 0.10698 -0.66
## scale(strengthIn) 0.003429 0.05855 0.32 0.50
## Residual 0.664514 0.81518
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.38996 0.06988 282.23489 5.580 5.63e-08 ***
## scale(strengthOut) 0.24773 0.02551 103.26253 9.710 3.19e-16 ***
## scale(strengthIn) 0.05136 0.02525 45.71799 2.034 0.0478 *
## numID -0.02195 0.00443 192.91915 -4.955 1.57e-06 ***
## scale(length) 0.04049 0.02176 1933.14650 1.861 0.0629 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.233
## scl(strngI) 0.106 -0.012
## numID -0.805 -0.361 -0.019
## scal(lngth) -0.087 -0.066 0.143 0.058
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
People feel experiences with more causes are more representative of self. Similar, but weaker, effect for experiences caused by more experiences.
m<-lmer( scale(Rep) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Rep) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5090.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3859 -0.5487 0.0731 0.6374 2.9952
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.25619 0.5062
## scale(outdegree) 0.01300 0.1140 -0.36
## scale(indegree) 0.01362 0.1167 -0.14 0.00
## Residual 0.62194 0.7886
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.366e-01 7.179e-02 2.812e+02 4.688 4.30e-06 ***
## scale(outdegree) 1.562e-01 2.577e-02 6.217e+01 6.062 8.66e-08 ***
## scale(indegree) 9.478e-02 2.992e-02 4.393e+01 3.168 0.002793 **
## numID -1.786e-02 4.628e-03 1.982e+02 -3.860 0.000153 ***
## scale(length) 1.825e-02 2.139e-02 1.915e+03 0.853 0.393570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.183
## scale(ndgr) 0.160 -0.073
## numID -0.806 -0.239 -0.152
## scal(lngth) -0.084 -0.083 0.113 0.054
m<-lmer( scale(Rep) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Rep) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5065.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4235 -0.5514 0.0586 0.6461 2.9458
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.253668 0.50365
## scale(strengthOut) 0.006451 0.08032 -0.66
## scale(strengthIn) 0.016450 0.12826 0.14 0.65
## Residual 0.617913 0.78607
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.663e-01 7.107e-02 2.943e+02 5.154 4.68e-07 ***
## scale(strengthOut) 1.750e-01 2.302e-02 1.595e+02 7.602 2.36e-12 ***
## scale(strengthIn) 1.305e-01 3.091e-02 4.372e+01 4.220 0.000121 ***
## numID -1.911e-02 4.537e-03 2.032e+02 -4.211 3.81e-05 ***
## scale(length) 2.974e-02 2.114e-02 1.966e+03 1.407 0.159606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.237
## scl(strngI) 0.159 0.092
## numID -0.799 -0.343 -0.052
## scal(lngth) -0.078 -0.059 0.121 0.051
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Experiences caused by more experiences have more qualitative positive sentiment.
m<-lmer( scale(vad_comp) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -5.9e+01
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_comp) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 6252.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1215 -0.2884 -0.1403 0.2995 2.9679
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.052467 0.22906
## scale(outdegree) 0.001345 0.03667 1.00
## scale(indegree) 0.018997 0.13783 0.54 0.54
## Residual 0.928431 0.96355
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.975e-02 5.256e-02 3.270e+02 1.708 0.08865 .
## scale(outdegree) 4.230e-03 2.449e-02 1.747e+02 0.173 0.86306
## scale(indegree) 9.365e-02 3.326e-02 3.969e+01 2.816 0.00754 **
## numID -4.418e-03 2.748e-03 1.137e+02 -1.608 0.11063
## scale(length) -1.513e-02 2.271e-02 1.373e+03 -0.666 0.50522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.190
## scale(ndgr) 0.185 -0.050
## numID -0.828 -0.108 -0.020
## scal(lngth) -0.139 -0.108 0.086 0.149
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(vad_comp) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -3.9e+00
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_comp) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 6249.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3313 -0.2978 -0.1362 0.2809 2.9859
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.0580113 0.24086
## scale(strengthOut) 0.0009226 0.03037 1.00
## scale(strengthIn) 0.0201867 0.14208 0.53 0.53
## Residual 0.9241950 0.96135
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.412e-02 5.336e-02 3.320e+02 1.764 0.07867 .
## scale(strengthOut) -4.679e-03 2.474e-02 1.861e+02 -0.189 0.85021
## scale(strengthIn) 1.010e-01 3.413e-02 4.409e+01 2.958 0.00496 **
## numID -4.506e-03 2.822e-03 1.217e+02 -1.597 0.11292
## scale(length) -1.402e-02 2.269e-02 1.364e+03 -0.618 0.53667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.182
## scl(strngI) 0.199 -0.098
## numID -0.825 -0.108 -0.031
## scal(lngth) -0.123 -0.070 0.100 0.134
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Experiences with more experiences causing them are qualitatively more positive
m<-lmer( scale(vad_pos) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -5.5e+00
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_pos) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 6280.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4990 -0.5707 -0.4513 0.2449 4.9708
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.0308695 0.17570
## scale(outdegree) 0.0002493 0.01579 -1.00
## scale(indegree) 0.0135756 0.11651 0.00 0.00
## Residual 0.9512739 0.97533
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.235e-02 5.161e-02 4.055e+02 1.014 0.31100
## scale(outdegree) -1.533e-02 2.340e-02 1.365e+03 -0.655 0.51236
## scale(indegree) 8.794e-02 3.098e-02 3.174e+01 2.838 0.00784 **
## numID -2.678e-03 2.682e-03 1.587e+02 -0.998 0.31959
## scale(length) -5.457e-02 2.271e-02 1.451e+03 -2.403 0.01639 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.174
## scale(ndgr) 0.161 -0.192
## numID -0.863 -0.210 -0.121
## scal(lngth) -0.153 -0.133 0.077 0.159
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(vad_pos) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+01
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_pos) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 6280
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4112 -0.5711 -0.4542 0.2452 4.9415
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.031845 0.17845
## scale(strengthOut) 0.000428 0.02069 -1.00
## scale(strengthIn) 0.008238 0.09077 -0.05 0.05
## Residual 0.953023 0.97623
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.653e-02 5.203e-02 4.084e+02 1.086 0.2780
## scale(strengthOut) -1.947e-02 2.385e-02 1.698e+03 -0.816 0.4144
## scale(strengthIn) 8.597e-02 2.921e-02 2.930e+01 2.943 0.0063 **
## numID -3.003e-03 2.714e-03 1.658e+02 -1.107 0.2701
## scale(length) -5.471e-02 2.268e-02 1.439e+03 -2.412 0.0160 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.180
## scl(strngI) 0.164 -0.254
## numID -0.866 -0.221 -0.142
## scal(lngth) -0.143 -0.103 0.085 0.148
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
No negative effects
m<-lmer( scale(vad_neg) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_neg) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 6271.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2895 -0.4122 -0.2912 -0.1984 6.5233
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.064206 0.25339
## scale(outdegree) 0.005104 0.07144 0.28
## scale(indegree) 0.001228 0.03504 -0.97 -0.05
## Residual 0.932602 0.96571
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.519e-02 5.541e-02 3.214e+02 -1.176 0.240
## scale(outdegree) -2.064e-02 2.589e-02 8.614e+01 -0.797 0.427
## scale(indegree) -3.201e-03 2.329e-02 1.446e+02 -0.137 0.891
## numID 3.234e-03 3.043e-03 1.447e+02 1.063 0.290
## scale(length) -1.277e-03 2.331e-02 1.576e+03 -0.055 0.956
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.172
## scale(ndgr) 0.146 -0.187
## numID -0.846 -0.120 -0.210
## scal(lngth) -0.135 -0.107 0.099 0.131
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(vad_neg) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_neg) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 6271.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2743 -0.4125 -0.2928 -0.1973 6.5147
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.066791 0.25844
## scale(strengthOut) 0.003819 0.06179 0.72
## scale(strengthIn) 0.001439 0.03793 -0.86 -0.27
## Residual 0.933385 0.96612
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.008e-02 5.533e-02 3.313e+02 -1.086 0.278
## scale(strengthOut) -1.216e-02 2.601e-02 9.089e+01 -0.468 0.641
## scale(strengthIn) 1.618e-03 2.431e-02 8.086e+01 0.067 0.947
## numID 3.063e-03 3.034e-03 1.505e+02 1.010 0.314
## scale(length) -2.765e-03 2.315e-02 1.542e+03 -0.119 0.905
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.147
## scl(strngI) 0.165 -0.243
## numID -0.841 -0.049 -0.223
## scal(lngth) -0.121 -0.070 0.104 0.118
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
No effect of causes or caused by on number of words
m<-glmer(nwords ~ outdegree + indegree + numID + scale(length) + ( outdegree + indegree | subID), data=fullData, family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: nwords ~ outdegree + indegree + numID + scale(length) + (outdegree +
## indegree | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 12504.6 12567.3 -6241.3 12482.6 2204
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3076 -0.7071 -0.1331 0.5397 12.0926
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.568425 0.75394
## outdegree 0.002340 0.04838 0.12
## indegree 0.002723 0.05218 -0.38 0.26
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9334800 0.0811461 23.827 <2e-16 ***
## outdegree -0.0123978 0.0077872 -1.592 0.111
## indegree -0.0005592 0.0090943 -0.061 0.951
## numID 0.0006189 0.0056419 0.110 0.913
## scale(length) 0.0862643 0.0097761 8.824 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) outdgr indegr numID
## outdegree -0.099
## indegree -0.081 0.116
## numID -0.738 0.044 -0.178
## scal(lngth) -0.034 -0.047 0.085 0.014
Experiences with more causes are more broad.
m<-lmer( scale(Breadth) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Breadth) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5255.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38126 -0.64650 -0.00537 0.65203 2.99689
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.254937 0.50491
## scale(outdegree) 0.033977 0.18433 -0.08
## scale(indegree) 0.001751 0.04185 -0.01 0.94
## Residual 0.703320 0.83864
## Number of obs: 1975, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.435e-01 7.291e-02 2.532e+02 3.339 0.000967 ***
## scale(outdegree) 9.815e-02 3.180e-02 5.899e+01 3.086 0.003086 **
## scale(indegree) -2.257e-02 2.359e-02 4.733e+00 -0.957 0.385073
## numID -1.430e-02 4.654e-03 1.716e+02 -3.072 0.002472 **
## scale(length) 4.908e-02 2.276e-02 1.916e+03 2.157 0.031163 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.224
## scale(ndgr) 0.144 0.113
## numID -0.800 -0.187 -0.114
## scal(lngth) -0.086 -0.075 0.134 0.051
m<-lmer( scale(Breadth) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+03
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Breadth) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5276
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3666 -0.6810 -0.0020 0.6681 3.1794
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2663596 0.51610
## scale(strengthOut) 0.0004799 0.02191 1.00
## scale(strengthIn) 0.0055609 0.07457 0.24 0.24
## Residual 0.7270389 0.85267
## Number of obs: 1975, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.239e-01 7.090e-02 2.030e+02 3.158 0.00183 **
## scale(strengthOut) 6.527e-02 2.299e-02 4.132e+02 2.840 0.00474 **
## scale(strengthIn) 7.710e-03 2.789e-02 2.932e+00 0.276 0.80054
## numID -1.265e-02 4.571e-03 1.580e+02 -2.767 0.00633 **
## scale(length) 6.804e-02 2.257e-02 1.890e+03 3.015 0.00261 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.125
## scl(strngI) 0.135 -0.107
## numID -0.782 -0.044 -0.060
## scal(lngth) -0.064 -0.036 0.152 0.030
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Experiences with more causes are perceived as more distinct/different.
m<-lmer( scale(Dist) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Dist) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5135.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9072 -0.5359 0.1170 0.6383 2.6875
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2933625 0.54163
## scale(outdegree) 0.0256675 0.16021 -0.38
## scale(indegree) 0.0002987 0.01728 -0.55 -0.56
## Residual 0.6454951 0.80343
## Number of obs: 1985, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.726e-01 7.529e-02 2.591e+02 2.292 0.02270 *
## scale(outdegree) 8.732e-02 2.912e-02 8.263e+01 2.999 0.00358 **
## scale(indegree) 3.735e-02 2.118e-02 6.440e+02 1.763 0.07837 .
## numID -9.835e-03 4.891e-03 1.879e+02 -2.011 0.04577 *
## scale(length) -8.188e-04 2.192e-02 1.945e+03 -0.037 0.97020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.189
## scale(ndgr) 0.117 -0.125
## numID -0.806 -0.264 -0.151
## scal(lngth) -0.087 -0.081 0.142 0.056
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(Dist) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Dist) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5121.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8859 -0.5442 0.1147 0.6225 2.6721
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.291166 0.53960
## scale(strengthOut) 0.019970 0.14132 -0.53
## scale(strengthIn) 0.003752 0.06126 0.02 0.22
## Residual 0.641282 0.80080
## Number of obs: 1985, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.969e-01 7.513e-02 2.660e+02 2.621 0.00927 **
## scale(strengthOut) 1.358e-01 2.827e-02 5.135e+01 4.804 1.39e-05 ***
## scale(strengthIn) 1.568e-02 2.575e-02 1.906e+01 0.609 0.54975
## numID -1.147e-02 4.844e-03 1.927e+02 -2.367 0.01894 *
## scale(length) -6.898e-04 2.178e-02 1.930e+03 -0.032 0.97474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.207
## scl(strngI) 0.120 -0.078
## numID -0.805 -0.319 -0.085
## scal(lngth) -0.079 -0.063 0.139 0.050
fullData$SminO <- fullData$SO_1 - fullData$SO_2
m<-lmer( SminO ~ SE + ( 1 | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SminO ~ SE + (1 | subID)
## Data: fullData
##
## REML criterion at convergence: 17813.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8301 -0.5846 -0.1584 0.6071 2.8011
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 287 16.94
## Residual 1191 34.50
## Number of obs: 1776, groups: subID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 20.1316 6.8611 197.7275 2.934 0.00374 **
## SE 0.4568 2.9956 200.2941 0.152 0.87896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## SE -0.974
m<-lmer(Chan ~ page + ( page | subID), data=fullData)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00201605 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ page + (page | subID)
## Data: fullData
##
## REML criterion at convergence: 7451.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5506 -0.5188 0.1435 0.6380 2.4692
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.7239 0.8508
## page 2.8202 1.6794 -0.37
## Residual 1.8739 1.3689
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.28397 0.09343 139.75154 56.555 <2e-16 ***
## page 0.53139 0.39896 155.86021 1.332 0.185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## page -0.686
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00201605 (tol = 0.002, component 1)
m<-lmer(Chan ~ pageW + ( pageW | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageW + (pageW | subID)
## Data: fullData
##
## REML criterion at convergence: 7450
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5729 -0.5228 0.1492 0.6423 2.4633
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.7045 0.8393
## pageW 2.1633 1.4708 -0.31
## Residual 1.8739 1.3689
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.25817 0.09159 146.51342 57.41 <2e-16 ***
## pageW 0.68403 0.38436 163.86429 1.78 0.077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pageW -0.669
m<-lmer(Chan ~ pageOut + ( pageOut | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageOut + (pageOut | subID)
## Data: fullData
##
## REML criterion at convergence: 7423
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5889 -0.5275 0.1288 0.6339 2.2266
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.9007 0.949
## pageOut 5.2869 2.299 -0.70
## Residual 1.8388 1.356
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.18192 0.09275 168.21829 55.872 < 2e-16 ***
## pageOut 1.32770 0.34736 165.28688 3.822 0.000187 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pageOut -0.705
m<-lmer(Chan ~ pageOutW + ( pageOutW | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageOutW + (pageOutW | subID)
## Data: fullData
##
## REML criterion at convergence: 7424.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5684 -0.5271 0.1284 0.6323 2.2400
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.8643 0.9297
## pageOutW 4.8983 2.2132 -0.65
## Residual 1.8400 1.3565
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.16388 0.09199 168.14558 56.134 < 2e-16 ***
## pageOutW 1.40907 0.35061 164.58161 4.019 8.88e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pageOutW -0.692
m<-lmer(Chan ~ hub + ( hub | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ hub + (hub | subID)
## Data: fullData
##
## REML criterion at convergence: 7374.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6191 -0.5154 0.1350 0.6102 2.6256
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.8484 0.9211
## hub 0.6631 0.8143 -0.56
## Residual 1.7554 1.3249
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.14426 0.08313 180.94385 61.881 < 2e-16 ***
## hub 0.63695 0.10737 199.75811 5.932 1.3e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## hub -0.601
m<-lmer(Chan ~ hubW + ( hubW | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ hubW + (hubW | subID)
## Data: fullData
##
## REML criterion at convergence: 7370.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6127 -0.5366 0.1385 0.6163 2.6130
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.8089 0.8994
## hubW 0.5306 0.7284 -0.53
## Residual 1.7619 1.3274
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.15300 0.07975 188.71394 64.614 < 2e-16 ***
## hubW 0.69360 0.10529 232.85966 6.588 2.95e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## hubW -0.548
fullShort <- do.call(data.frame, # Replace Inf in data by NA
lapply(fullShort,
function(x) replace(x, is.infinite(x), NA)))
corMat <- fullShort %>% select(edgeTot:NFC) %>% cor(fullShort,use="pairwise.complete.obs")
outphm <- pheatmap(corMat, fontsize_row = 6, fontsize_col = 6, angle_col = 45, angle_row =45, width=100, height = 200 )
heatmaply_cor(round(corMat,3), Rowv=outphm[[1]], Colv=outphm[[2]], revC=TRUE, fontsize_row = 2.5, fontsize_col = 2.5, angle_col = 45, angle_row =45, limits = c(-1, 1), colors = colorRampPalette(rev(brewer.pal(n = 7, name =
"RdYlBu")))(100) )
fullShort %>% select(vad_compAg, MAIA:NFC) %>% corToOne(., "vad_compAg")
## Loading required package: corrr
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
fullShort %>% select(vad_compAg, MAIA:NFC) %>% plotCorToOne(., "vad_compAg")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(edgeTot, MAIA:NFC) %>% corToOne(., "edgeTot")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(edgeTot, MAIA:NFC) %>% plotCorToOne(., "edgeTot")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(numID, MAIA:NFC) %>% corToOne(., "numID")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(numID, MAIA:NFC) %>% plotCorToOne(., "numID")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(dense, MAIA:NFC) %>% corToOne(., "dense")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(dense, MAIA:NFC) %>% plotCorToOne(., "dense")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(aveDist, MAIA:NFC) %>% corToOne(., "aveDist")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(aveDist, MAIA:NFC) %>% plotCorToOne(., "aveDist")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% corToOne(., "Val_1_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_1_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% corToOne(., "Val_2_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_2_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% corToOne(., "Fund_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% plotCorToOne(., "Fund_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% corToOne(., "Rep_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% plotCorToOne(., "Rep_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% corToOne(., "Chan_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% plotCorToOne(., "Chan_Homoph")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% corToOne(., "recip")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% plotCorToOne(., "recip")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(cohes, MAIA:NFC) %>% corToOne(., "cohes")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(cohes, MAIA:NFC) %>% plotCorToOne(., "cohes")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(modular, MAIA:NFC) %>% corToOne(., "modular")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(modular, MAIA:NFC) %>% plotCorToOne(., "modular")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% corToOne(., "recip")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% plotCorToOne(., "recip")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDeg, MAIA:NFC) %>% corToOne(., "sdDeg")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDeg, MAIA:NFC) %>% plotCorToOne(., "sdDeg")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDegW, MAIA:NFC) %>% corToOne(., "sdDegW")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDegW, MAIA:NFC) %>% plotCorToOne(., "sdDegW")
## [1] "All required packages attached"
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'